We build retrieval-augmented generation (RAG) systems that answer from your own documents, contracts, tickets and wikis, with sources your team can check. Fixed price, evaluated against your real questions, and deployed in your own cloud.
By Dnyanesh Chandewar, Founder, CyberCore
RAG lets a language model look up relevant passages from your own content before it answers, instead of relying on what it memorised in training. That makes answers current, traceable to a source, and limited to what you allow. If the model needs to know your policies, contracts, product docs or ticket history, RAG is usually the first and cheapest approach to try. Fine-tuning is a different tool for a different problem; see how much RAG costs and when fine-tuning is cheaper, and the plain-English primer What Is RAG?
Typical builds: internal knowledge assistants for legal, compliance, customer success and sales engineering; customer-facing documentation search; contract and policy question-answering; and retrieval layers that give an AI agent grounded access to company data. Every build includes document ingestion and chunking tuned to your content, hybrid retrieval and re-ranking where it earns its keep, citation in every answer, access controls that mirror your existing permissions, and an evaluation set built from real questions your team asks. One example: an internal legal knowledge base with AI search that cut research time by 65%, deployed in 4 weeks (see case studies).
Scoping (free): a call with me, then a written scope covering the content sources, who may see what, how success is measured, and a fixed price. Build (weeks 1–2): ingestion, retrieval and a working assistant on your real documents. Evaluation and hand-over (weeks 3–4): accuracy tested against your own question set with agreed pass thresholds, then deployment, monitoring of first live traffic, and hand-over of code, prompts, evaluation suite and runbook. Most single-source RAG builds fit this window; multi-source or regulated-data systems take longer and the timeline is agreed in writing first.
The price of a RAG system is driven by a handful of things: how many content sources you connect and how messy they are, whether permissions must be enforced per user, how accurate answers need to be (which sets the evaluation effort), integration into your existing tools, and where it has to run for data-residency reasons. Running cost afterwards is mostly model usage and hosting. We quote a fixed price after scoping rather than an hourly estimate, so you know the full build cost before you commit.
We choose the model, vector store and orchestration per job: Claude, GPT or Gemini models, Postgres/pgvector or a managed vector database, and a lightweight custom pipeline or LangGraph where multi-step reasoning is needed. Related: AI agent development when the system must act as well as answer, and Claude and OpenAI API integration when you already have a product to wire it into.
Every CyberCore RAG build is quoted as a fixed price after a free scoping call. The main drivers are the number and quality of content sources, per-user permissions, the accuracy bar and evaluation effort, and integrations. You know the full build price before work starts.
Most single-source RAG builds go from scope to production in 2–4 weeks. Multi-source or regulated-data systems take longer, and the timeline is agreed in the written scope.
Start with RAG when the model needs your documents or data, because it is faster to build, easier to update and gives citations. Fine-tuning suits changing style or behaviour at very high volume. We tell you honestly which fits during scoping.
Answers are grounded in retrieved passages and cite their sources, the system declines when nothing relevant is found, and an evaluation set built from your real questions is run before go-live and after every change.
You do. Code, prompts, the evaluation suite and documentation are handed over and deployed in your own infrastructure.
Book a 30-minute call. You'll leave with a clear view of what it would take and a fixed price if it makes sense.
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